Citation

Hasan, Md Rakibul and Goh, Hui Ngo and Ting, Choo Yee and Quek, Albert and Haque, Radiah (2025) Methods and challenges in job recommendation systems: A review. In: 4th International Conference on Computer, Information Technology and Intelligent Computing, CITIC 2024, 23 July 2024 - 25 July 2024, Virtual, Online.

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Abstract

As digital platforms reshape the landscape of recruitment and employment, Job Recommendation Systems (JRS) play a crucial role in aligning candidates with suitable job opportunities. This comprehensive review examines technological advancements, methodological approaches, and the impacts of proposed systems in JRS. Specifically, this paper provides an in-depth exploration of JRS by detailing the methodologies followed and challenges encountered in the development and implementation of such systems across existing studies. The paper further categorizes the major techniques used for job recommendation, focusing on content-based filtering, collaborative filtering, and hybrid approaches. Additionally, the research in this paper examines the classification of hybrid approaches within JRS literature, explaining their grouping according to established taxonomies in recommender systems. Subsequently, the paper addresses several key challenges: data privacy, dynamic job and candidate profiles, and bias mitigation with fairness in recommendations. It further highlights some of the challenges and potential solutions for developing future job recommender systems more effectively and fairly.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Data management, Careers and professions, Review
Subjects:	Z Bibliography. Library Science. Information Resources > ZA3038-5190 Information resources (General)
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	10 Dec 2025 06:24
Last Modified:	13 Dec 2025 07:31
URII:	http://shdl.mmu.edu.my/id/eprint/15023

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